This study explores the integration of the ChatGPT API with GPT-4 model and Microsoft Copilot Studio on the Microsoft Teams platform to develop an intelligent tutoring system. Designed to provide instant support to students, the system dynamically adjusts educational content in response to the learners' progress and feedback. Utilizing advancements in natural language processing and machine learning, it interprets student inquiries, offers tailored feedback, and facilitates the educational journey. Initial implementation highlights the system's potential in boosting students' motivation and engagement, while equipping educators with critical insights into the learning process, thus promoting tailored educational experiences and enhancing instructional effectiveness.

Can student behavior be anticipated in real-time so that an intelligent tutor system can adapt its content to keep the student engaged? Current methods detect affective states of students during learning session to determine their engagement levels but apply the learning in next session in the form of intervention policies and tutor responses. However, if students' imminent behavioral action could be anticipated from their affective states in real-time, this could lead to much more responsive intervention policies by the tutor and assist in keeping the student engaged in an activity, thereby increasing tutor efficacy as well as student engagement levels. In this paper we explore if there exist any links between a student's affective states and his/her imminent behavior action in RoboTutor, an intelligent tutor system for children to learn math, reading and writing. We then exploit our findings to develop a real-time student behavior prediction module.